Dataset Information

Integrating 13 Microarrays to Construct a 6 RNA-binding proteins Prognostic Signature for Gastric Cancer patients.

ABSTRACT: Background: It has been confirmed in many tumors that RNA-binding proteins (RBPs) will affect the progress of cancer, but there is still a lack of large-scale research in gastric cancer (GC). Methods: We obtained 13 microarray mRNA expression profiles of the GPL570 platform, and extracted expression from them after integration to analyze the expression differences of RBPs. Enrichment analysis studies the role of these RBPs in GC. Univariate, Lasso and multivariate Cox regression analysis are used to identify independent prognostic hub RBPs, thereby constructing and verifying a prognostic signature. External data and rt-PCR verified the expression of hub RBPs. Results: We have identified 51 dysregulated RBPs in GC. Enrichment analysis shows that it can mainly participate in RNA decomposition, modification, processing, etc. and affect the progress of GC. After multiple statistical analysis, six independent prognostic RBPs of GC were determined and a prognostic signature was developed. According to the median risk value, the training cohort was divided into high-risk and low-risk groups. Considering the clinical characteristics, in training, testing, and complete cohorts, the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, which was confirmed by the time-dependent receiver operating characteristic curve. Univariate and multivariate Cox regression analysis of independent prognostic ability of risk score. In addition, we constructed and verified a nomogram based on the prognostic signature, showing accurate prediction performance. rt-PCR and external data verification are consistent with our conclusions. Conclusion: This study analyzed the overall expression of RPBs in GC and explored its mechanism. A new prognostic signature was developed and verified. A nomogram has also been established and verified, which helps to improve the treatment strategy for GC.

SUBMITTER: Zhou L

PROVIDER: S-EPMC8247375 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeThis study aimed to construct a prognostic signature consisting of immune-related RNA-binding proteins (RBPs) to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) effectively.MethodsThe transcriptome and clinical data of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. First, we ascertained the immunological differences in HNSCC, through single-sample gene set enrichment analysis, stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) deconvolution algorithm. Then we used univariate proportional hazards (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to screen immune-related RBPs and acquire the risk score of each sample. Subsequently, we further investigated the difference in prognosis, immune status, and tumor mutation burden in high- and low-risk groups. Finally, the efficacy of immunotherapy was measured by the tumor immune dysfunction and exclusion (TIDE) score.ResultsWe derived 15 immune-related RBPs, including FRMD4A, ASNS, RAB11FIP1, FAM120C, CFLAR, CTTN, PLEKHO1, SELENBP1, CHCHD2, NPM3, ATP2A3, CFDP1, IGF2BP2, NQO1, and DENND2D. There were significant differences in the prognoses of patients in the high- and low-risk groups in the training set (p < 0.001) and the validation set (p < 0.01). Furthermore, there were statistical differences between the high-risk group and low-risk group in immune cell infiltration and pathway and tumor mutation load (p < 0.001). In the end, we found that patients in the low-risk group were more sensitive to immunotherapy (p < 0.001), and then we screened 14 small-molecule chemotherapeutics with higher sensitivity to the high-risk group (p < 0.001).ConclusionThe study constructed a prognostic signature of HNSCC, which might guide clinical immunotherapy in the future.

Project description:Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

Project description:BackgroundAlthough breast cancer (BC) treatment has entered the era of precision therapy, the prognosis is good in the case of comprehensive multimodal treatment such as neoadjuvant, endocrine, and targeted therapy. However, due to its high heterogeneity, some patients still cannot benefit from conventional treatment and have poor survival prognoses. Amino acids and their metabolites affect tumor development, alter the tumor microenvironment, play an increasingly obvious role in immune response and regulation of immune cell function, and are involved in acquired and innate immune regulation; therefore, amino acid metabolism is receiving increasing attention.MethodsBased on public datasets, we carried out a comprehensive transcriptome and single-cell sequencing investigation. Then we used 2.5 Weighted Co-Expression Network Analysis (WGCNA) and Cox to evaluate glutamine metabolism-related genes (GRGs) in BC and constructed a prognostic model for BC patients. Finally, the expression and function of the signature key gene SNX3 were examined by in vitro experiments.ResultsIn this study, we constituted a risk signature to predict overall survival (OS) in BC patients by glutamine-related genes. According to our risk signature, BC patients can obtain a Prognostic Risk Signature (PRS), and the response to immunotherapy can be further stratified according to PRS. Compared with traditional clinicopathological features, PRS demonstrated robust prognostic power and accurate survival prediction. In addition, altered pathways and mutational patterns were analyzed in PRS subgroups. Our study sheds some light on the immune status of BC. In in vitro experiments, the knockdown of SNX3, an essential gene in the signature, resulted in a dramatic reduction in proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cell lines.ConclusionWe established a brand-new PRS consisting of genes associated with glutamine metabolism. It expands unique ideas for the diagnosis, treatment, and prognosis of BC.

Dataset Information

Integrating 13 Microarrays to Construct a 6 RNA-binding proteins Prognostic Signature for Gastric Cancer patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets